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python-markupsafe-0.11-7.fc18.1302091146kf.armv6hl: A library for safe markup escaping.

python-markupsafe-0.9.3-1.x86_64: This package implements a XML/HTML/XHTML markup safe string for Python.

python-markdown-2.0.3-1.fc13.noarch: This is a Python implementation of John Gruber's Markdown. It is almost completely compliant with the reference implementation, though there are a few known issues.

python-manuel-1.1.1-2.lbn13.noarch: Manuel lets you build tested documentation.

python-mailinglogger-3.7.0-2.lbn13.noarch: mailinglogger

python-m01.stub-0.5.7-1.lbn13.noarch: This package provides a mongodb server stub setup for python doctests.

python-m01.session-0.5.0-1.lbn13.noarch: This package provides a session implementation based on mongodb for Zope3.

python-m01.searcher-0.5.1-1.lbn13.noarch: This package provides a mongodb based search filter implementation for Zope3.

python-m01.remote-0.5.1-1.lbn13.noarch: This package offers a remote processor. This remote processor is implemented as a simple object using the mongodb as storage. The processor can execute pre defined jobs in another thread. It is also possible to run jobs at specific time using the different scheduler items. The RemoteProcessor uses two different processor. One processes jobs and the other pickes items from the scheduler and is adding jobs. This separation is useful if you implement a distributed concept. This means one or more application can schedule job items based on the given scheduler items. And another application is processing jobs and doesn't know about how to scheduling next items. Since we use this remote scheduler for low CPU intensive jobs, we offer multi processing. This is done by running more then one worker in the main worker thread. If you use subprocess for your job processing, you will get a real multiprocessing processor which isn't limited to the current python process. You can configure the amount of threads which a job worker can start in the remote processor. See jobWorkerArguments/maxThreads. By default this number uses the amount of CPU installed on your machine. The implementation uses a mongodb as a storage for it's component. This means jobs, job factories and scheduler items get stored in the mongodb using the ORM concept given from m01.mongo. See p01.remote for a ZODB based remote processor implementation but take care the p01.remote implementation doesn't provide the worker and scheduler processor separation. At least not yet.

python-m01.mongofake-1.0.0-1.lbn13.noarch: This package provides a FAKE mongoDB implementation and is quite handy for testing.

python-m01.mongo-0.10.2-1.lbn13.noarch: This package provides a mongodb object mapper framework including zope transaction support based on some core zope component libraries. This package can get used with or without zope.persistent and as a full replacement for the ZODB. The package is not heavy based on zope itself and can get used in any python project which requires a bridge from mongodb to python object.

python-m01.grid-0.5.0-2.lbn13.noarch: This package provides a mongodb grid based file implementation for Zope3.

python-m01.fs-0.5.1-2.lbn13.noarch: This package provides a mongodb file implementation without GridFS for Zope3. This means, such files are limited to the mongo document size, currently 16MB. As you probably know, you should not use GridFS for small binary data because you will double the number of queries. This package will offer a file which stores the meta and file data in one document.

python-m01.form-0.5.1-1.lbn13.noarch: This package provides z3c.form widget converter for m01.mongo components.

python-m01.bayes-0.5.0-1.lbn13.noarch: This package provides a bayesian classifier based on for m01.mongo.

python-lxml-2.3.6-1.lbn13.x86_64: lxml provides a Python binding to the libxslt and libxml2 libraries. It follows the ElementTree API as much as possible in order to provide a more Pythonic interface to libxml2 and libxslt than the default bindings. In particular, lxml deals with Python Unicode strings rather than encoded UTF-8 and handles memory management automatically, unlike the default bindings.

python-lxml-2.3.5-1.fc18.armv6hl: lxml provides a Python binding to the libxslt and libxml2 libraries. It follows the ElementTree API as much as possible in order to provide a more Pythonic interface to libxml2 and libxslt than the default bindings. In particular, lxml deals with Python Unicode strings rather than encoded UTF-8 and handles memory management automatically, unlike the default bindings.

python-llist-0.4-1.lbn13.x86_64: llist is an extension module for CPython providing basic linked list data structures. Collections implemented in the llist module perform well in problems which rely on fast insertions and/or deletions of elements in the middle of a sequence. For this kind of workload, they can be significantly faster than collections.deque or standard Python lists. This extension requires CPython 2.5 or newer (3.x is supported). If you are looking for an implementation of linked lists in pure Python, visit http://github.com/rgsoda/pypy-llist/ The pypy-llist module has the same API as this extension, but is significantly slower in CPython. Currently llist provides the following types of linked lists: dllist - a doubly linked list sllist - a singly linked list

python-linkedin-0.1.5-2.lbn13.noarch: from linkedin import * *Get an authorization url for your user* l = LinkedinAPI(api_key='*your app key*', api_secret='*your app secret*', callback_url='http://www.example.com/callback/', permissions=["r_network"]) auth_props = l.get_authentication_tokens() auth_url = auth_props['auth_url'] oauth_token_secret = auth_props['oauth_token_secret'] print 'Connect with LinkedIn via: %s' % auth_url If you leave callback_url blank, you can get the oauth_verifier from the web browser. It is a five-digit integer. The permissions parameter is optional. It can be a list or string. The [list of permissions](https://developer.linkedin.com/documents/authentication) is in the LinkedIn API documentation. Once you click "Allow" be sure that there is a URL set up to handle getting finalized tokens and possibly adding them to your database to use their information at a later date. \n\n' oauth_token = *Grab oauth token from URL* oauth_verifier = *Grab oauth verifier from URL* l = LinkedinAPI(api_key='*your app key*', api_secret='*your app secret*', oauth_token=oauth_token, oauth_token_secret=session['linkedin_session_keys']['oauth_token_secret']) authorized_tokens = l.get_access_token(oauth_verifier) final_oauth_token = authorized_tokens['oauth_token'] final_oauth_token_secret = authorized_tokens['oauth_token_secret'] l = LinkedinAPI(api_key = '*your app key*', api_secret = '*your app secret*', oauth_token=final_tokens['oauth_token'], oauth_token_secret=final_tokens['oauth_token_secret']) profile = l.get('people/~', fields='first-name,last-name') print profile search = l.get('people-search', params={'keywords':'Hacker'}) print search feed = l.get('people/~/network/updates') print feed share_content = { "comment": "Posting from the API using JSON", "content": { "title": "A title for your share", "submitted-url": "http://www.linkedin.com", "submitted-image-url": "http://lnkd.in/Vjc5ec" }, "visibility": { "code": "anyone" } } share_update = l.post('people/~/shares', params=share_content) print share_update

python-ldap-2.4.9-1.lbn13.x86_64: python-ldap provides an object-oriented API for working with LDAP within Python programs. It allows access to LDAP directory servers by wrapping the OpenLDAP 2.x libraries, and contains modules for other LDAP-related tasks (including processing LDIF, LDAPURLs, LDAPv3 schema, etc.).

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